Detect outliers of sequence in stream.
Project description
StreamOutlierDetector
Detect outliers of sequence in stream.
In this project we have some assumption:
- This project works online that means has no idea about the future data
- This project forget older data (more than size of sample array)
- If more than half of sample array be in outlier then this project assume the majority is not outlier and calculate outlier detection for the sample again
Usage
install
pip install pyood
pip install --upgrade pyood # if you want update package
How use it
from pyood import OutlierDetector
outlier_detector = OutlierDetector(bound_factor_standard_deviation=3, window_size=20, size_initial_ignore=10)
is_outlier = outlier_detector.push(your_value)
if you want, you can use it with callback function
from pyood import OutlierDetector
def result(is_outlier):
print(is_outlier)
outlier_detector = OutlierDetector(bound_factor_standard_deviation=3, window_size=20, size_initial_ignore=10)
is_outlier = outlier_detector.push(value=your_value, callback=result)
Help
bound_factor_standard_deviation
is the factor that multiple with standard deviation. |value - mean| > bound_factor_standard_deviation * satandard deviation
is the outlier.
window_size
is the size of array is effective for finding outlier.
first_learning_number
is the number of first value we ignore and learn from them.
Warning ⚠ |
---|
if the outlier be in the first first_learning_number we return it is not outlier and more dangerous we learn it and ruined the mean and variance for a while |
Result
I test this class and show the functionality of it on a chart.
❌ are the outliers we detect.
🔵 are the normal values.
- are the bound of outlier detection.
Without bound | With bounds |
---|---|
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